Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery

Understand

We release the largest public ECG dataset of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats.

• Our goal is to enable semi-supervised ECG models to be made as well as to discover unknown subtypes of arrhythmia and anomalous ECG signal events.
• To this end, we propose an unsupervised representation learning task, evaluated in a semi-supervised fashion.
• We provide a set of baselines for different feature extractors that can be built upon.